Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104312
A. Koochakzadeh, P. Pal
This paper considers the problem of channel estimation for millimeter wave wireless communication channels. Many existing channel estimation approaches utilize the spatial sparsity of mmWave channels and employ compressive sensing based techniques to estimate the parameters of the channel, such as the Angles of Arrival (AoA) and Angles of Departure (AoD) of the channel paths. In this paper, we show how the problem of channel estimation can be converted into a fourth order tensor decomposition problem, which offers several benefits. Firstly, we do not need a grid-based search for the angles. More importantly, our algorithm is applicable for both uniform and non-uniform arrays at the transmitter and receiver. In particular, our method can exploit well-known benefits offered by the difference co-array of suitably designed sparse arrays and provably identify a larger number of channel paths compared to existing approaches1.
{"title":"Channel Estimation for Hybrid MIMO Communication with (Non-) Uniform Linear Arrays via Tensor Decomposition","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/SAM48682.2020.9104312","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104312","url":null,"abstract":"This paper considers the problem of channel estimation for millimeter wave wireless communication channels. Many existing channel estimation approaches utilize the spatial sparsity of mmWave channels and employ compressive sensing based techniques to estimate the parameters of the channel, such as the Angles of Arrival (AoA) and Angles of Departure (AoD) of the channel paths. In this paper, we show how the problem of channel estimation can be converted into a fourth order tensor decomposition problem, which offers several benefits. Firstly, we do not need a grid-based search for the angles. More importantly, our algorithm is applicable for both uniform and non-uniform arrays at the transmitter and receiver. In particular, our method can exploit well-known benefits offered by the difference co-array of suitably designed sparse arrays and provably identify a larger number of channel paths compared to existing approaches1.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"83 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80747713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104375
Yuchen Jiao, Yirong Ma, Yuantao Gu
Hyperspectral image clustering is an important and challenging problem, which aims to group image pixels according to the land cover information extracted from the spectrum. The spectrum observed at adjacent pixels are often highly-correlated, and leveraging such spatial correlation can greatly improve the clustering accuracy. Markov Random Field (MRF) is a powerful model to characterize such correlation. However, in this model the spatial parameter β often needs to be manually tuned, which brings difficulty in finding an optimal value. In this paper, we propose a novel hyperspectral clustering algorithm, which is able to learn parameter β from the data and thus achieves better performance. Specifically, we model the spectral information with Gaussian mixture model, and use variational expectation maximization method to complete the parameter estimation and clustering task. Experiments on both synthetic and real-world data sets verify the effectiveness of the proposed algorithm.
{"title":"Hyperspectral Image Clustering based on Variational Expectation Maximization","authors":"Yuchen Jiao, Yirong Ma, Yuantao Gu","doi":"10.1109/SAM48682.2020.9104375","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104375","url":null,"abstract":"Hyperspectral image clustering is an important and challenging problem, which aims to group image pixels according to the land cover information extracted from the spectrum. The spectrum observed at adjacent pixels are often highly-correlated, and leveraging such spatial correlation can greatly improve the clustering accuracy. Markov Random Field (MRF) is a powerful model to characterize such correlation. However, in this model the spatial parameter β often needs to be manually tuned, which brings difficulty in finding an optimal value. In this paper, we propose a novel hyperspectral clustering algorithm, which is able to learn parameter β from the data and thus achieves better performance. Specifically, we model the spectral information with Gaussian mixture model, and use variational expectation maximization method to complete the parameter estimation and clustering task. Experiments on both synthetic and real-world data sets verify the effectiveness of the proposed algorithm.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"102 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76105698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104223
Liangliang Li, Dan Luo, G. Bi, Xianpeng Wang, Dandan Meng
In this paper, a joint sparsity-inducing DOA estimation method is proposed for strictly noncircular sources with unknown mutual coupling. In the proposed method, two block-sparse recovery models are firstly formulated via parameterizing the steering vector without losing the array aperture. Then, taking the noncircularity of sources into account, a joint sparsity-inducing framework combined with reweighted l1 - norm optimization is constructed to estimate DOA, where the weighted matrix is structured by the noncircular MUSIC-like (NC MUSIC-like) spectrum function to strengthen the sparsity. Finally, DOA estimation can be realized via screening the position of nonzero blocks of the recovered block sparse matrix. Some simulations are implemented to demonstrate that the proposed method shows the effectiveness and superiority with unknown mutual coupling.
{"title":"Joint sparsity-inducing DOA estimation for strictly noncircular sources with unknown mutual coupling","authors":"Liangliang Li, Dan Luo, G. Bi, Xianpeng Wang, Dandan Meng","doi":"10.1109/SAM48682.2020.9104223","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104223","url":null,"abstract":"In this paper, a joint sparsity-inducing DOA estimation method is proposed for strictly noncircular sources with unknown mutual coupling. In the proposed method, two block-sparse recovery models are firstly formulated via parameterizing the steering vector without losing the array aperture. Then, taking the noncircularity of sources into account, a joint sparsity-inducing framework combined with reweighted l1 - norm optimization is constructed to estimate DOA, where the weighted matrix is structured by the noncircular MUSIC-like (NC MUSIC-like) spectrum function to strengthen the sparsity. Finally, DOA estimation can be realized via screening the position of nonzero blocks of the recovered block sparse matrix. Some simulations are implemented to demonstrate that the proposed method shows the effectiveness and superiority with unknown mutual coupling.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"46 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81275043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104248
Bingfan Liu, Baixiao Chen, Minglei Yang, Hui Xu
In this paper, we proposed a direction of arrival (DOA) estimation method based on sparse Bayesian learning (SBL) and a dynamic transmitted waveform design method for colocated multiple-input multiple-output (MIMO) radar. First, the SBL DOA estimation method is introduced into the MIMO radar with arbitrary transmitted waveforms. Our theoretical derivation shows that the estimation error of the SBL method is related to the transmitted waveforms. Then, we minimize the estimation error to obtain an updated transmitted waveforms, which will be transmitted in the next pulse repetition period. Numerical simulations show that compared with traditional orthogonal waveforms, the optimized waveforms could achieve a lower Cramér-Rao bound (CRB) and smaller DOA estimation error using the SBL method.
{"title":"DOA estimation using sparse Bayesian learning for colocated MIMO radar with dynamic waveforms","authors":"Bingfan Liu, Baixiao Chen, Minglei Yang, Hui Xu","doi":"10.1109/SAM48682.2020.9104248","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104248","url":null,"abstract":"In this paper, we proposed a direction of arrival (DOA) estimation method based on sparse Bayesian learning (SBL) and a dynamic transmitted waveform design method for colocated multiple-input multiple-output (MIMO) radar. First, the SBL DOA estimation method is introduced into the MIMO radar with arbitrary transmitted waveforms. Our theoretical derivation shows that the estimation error of the SBL method is related to the transmitted waveforms. Then, we minimize the estimation error to obtain an updated transmitted waveforms, which will be transmitted in the next pulse repetition period. Numerical simulations show that compared with traditional orthogonal waveforms, the optimized waveforms could achieve a lower Cramér-Rao bound (CRB) and smaller DOA estimation error using the SBL method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"14 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86202712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104362
C. Shi, Yijie Wang, Fei Wang, Jianjiang Zhou
In this paper, a low probability of intercept (LPI) performance optimization scheme for a joint radar-communications system (JRCS) is proposed, which is able to simultaneously estimate channel parameters from the target returns and decode the received communications signals. The primary objective is to improve the LPI performance of a JRCS by optimizing radar waveform design and communications power allocation while guaranteeing a predefined mutual information (MI) threshold for channel parameter estimation and a desired communications data rate (CDR) for data transmission, where both traditional isolated sub-band (TISB) and radar isolated sub-band (RISB) situations are discussed. Subsequently, the approach of Lagrange multipliers and the Karush-Kuhn-Tuckers (KKT) optimality conditions are derived to solve the resulting problems. Also, the successive interference cancellation (SIC) technique is employed to obtain the original communications signals free of any radar interference. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.
{"title":"LPI Performance Optimization Scheme for a Joint Radar-Communications System","authors":"C. Shi, Yijie Wang, Fei Wang, Jianjiang Zhou","doi":"10.1109/SAM48682.2020.9104362","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104362","url":null,"abstract":"In this paper, a low probability of intercept (LPI) performance optimization scheme for a joint radar-communications system (JRCS) is proposed, which is able to simultaneously estimate channel parameters from the target returns and decode the received communications signals. The primary objective is to improve the LPI performance of a JRCS by optimizing radar waveform design and communications power allocation while guaranteeing a predefined mutual information (MI) threshold for channel parameter estimation and a desired communications data rate (CDR) for data transmission, where both traditional isolated sub-band (TISB) and radar isolated sub-band (RISB) situations are discussed. Subsequently, the approach of Lagrange multipliers and the Karush-Kuhn-Tuckers (KKT) optimality conditions are derived to solve the resulting problems. Also, the successive interference cancellation (SIC) technique is employed to obtain the original communications signals free of any radar interference. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"66 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91393495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104292
Yijie Wang, C. Shi, Fei Wang, Jianjiang Zhou
This paper explores the low probability of intercept (LPI)-based optimal radar power allocation for target time delay estimation in joint radar and communications system. The basis of LPI-based optimal radar power allocation is to minimize the total power consumption of radar system under the constraints of a specified target time delay estimation accuracy and a quality of service (QoS) of communications base station. The expression for Cramér-Rao lower bound (CRLB) is analytically derived and applied to gauge target time delay estimation accuracy. The resulting optimization problem is non-convex, which can be solved by the approach of linear programming. Several numerical results are provided to verify the superiority of the proposed radar power allocation in terms of the LPI performance of radar system. It is also shown that the LPI performance of radar benefits from cooperation with the communication system by reducing the total radiated energy of radar.
{"title":"LPI-based Optimal Radar Power Allocation for Target Time Delay Estimation in Joint Radar and Communications System","authors":"Yijie Wang, C. Shi, Fei Wang, Jianjiang Zhou","doi":"10.1109/SAM48682.2020.9104292","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104292","url":null,"abstract":"This paper explores the low probability of intercept (LPI)-based optimal radar power allocation for target time delay estimation in joint radar and communications system. The basis of LPI-based optimal radar power allocation is to minimize the total power consumption of radar system under the constraints of a specified target time delay estimation accuracy and a quality of service (QoS) of communications base station. The expression for Cramér-Rao lower bound (CRLB) is analytically derived and applied to gauge target time delay estimation accuracy. The resulting optimization problem is non-convex, which can be solved by the approach of linear programming. Several numerical results are provided to verify the superiority of the proposed radar power allocation in terms of the LPI performance of radar system. It is also shown that the LPI performance of radar benefits from cooperation with the communication system by reducing the total radiated energy of radar.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"36 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84664251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104363
Chia-Hsiang Lin, J. Bioucas-Dias, Tzu-Hsuan Lin, Yen-Cheng Lin, Chao-Yuan Kao
Directly transmitting the huge amount of typical hyperspectral data acquired on satellite to the ground station is inefficient. This paper proposes a new compressed sensing strategy for hyperspectral imagery on spaceborne sensors systems. As the onboard computing/storage resources are limited, e.g., on CubeSat, the measurement strategy should be computationally very light. Furthermore, considering the limited communication bandwidth, a very low sampling rate is desired. Our encoder accounts for these requirements by separately recording the spatial details and the spectral information, both of which essentially require only simple averaging operators. Our measurement strategy naturally induces a reconstruction criterion that can be elegantly interpreted as a well-known fusion problem in satellite remote sensing, allowing the adoption of a convex optimization method for simple and fast decoding. Our method, termed spatial/spectral compressed encoder (SPACE), is experimentally evaluated on real hyperspectral data, showing superior efficacy in terms of both sampling rate and reconstruction accuracy.
{"title":"A New Hyperspectral Compressed Sensing Method for Efficient Satellite Communications","authors":"Chia-Hsiang Lin, J. Bioucas-Dias, Tzu-Hsuan Lin, Yen-Cheng Lin, Chao-Yuan Kao","doi":"10.1109/SAM48682.2020.9104363","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104363","url":null,"abstract":"Directly transmitting the huge amount of typical hyperspectral data acquired on satellite to the ground station is inefficient. This paper proposes a new compressed sensing strategy for hyperspectral imagery on spaceborne sensors systems. As the onboard computing/storage resources are limited, e.g., on CubeSat, the measurement strategy should be computationally very light. Furthermore, considering the limited communication bandwidth, a very low sampling rate is desired. Our encoder accounts for these requirements by separately recording the spatial details and the spectral information, both of which essentially require only simple averaging operators. Our measurement strategy naturally induces a reconstruction criterion that can be elegantly interpreted as a well-known fusion problem in satellite remote sensing, allowing the adoption of a convex optimization method for simple and fast decoding. Our method, termed spatial/spectral compressed encoder (SPACE), is experimentally evaluated on real hyperspectral data, showing superior efficacy in terms of both sampling rate and reconstruction accuracy.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"32 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90511280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104226
E. Grossi, M. Lops, L. Venturino
In this work, we consider the joint design of a surveillance radar and a multiple-input multiple-output communication system sharing the same bandwidth. In this framework, we maximize the energy efficiency at the communication system (i.e., the amount of information reliably delivered per unit of consumed energy) under a constraint on the minimum signal-to-disturbance ratio for each inspected range-azimuth resolution cell of the radar. The transmit powers of both systems, the space-time linear communication codebook, and the radar receive filters are the degrees of freedom for joint system optimization. The block coordinate ascent method is used to find an approximate solution to this optimization problem, and a numerical example is provided to show the merits of the proposed design strategy.
{"title":"Energy efficient communication with radar spectrum sharing","authors":"E. Grossi, M. Lops, L. Venturino","doi":"10.1109/SAM48682.2020.9104226","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104226","url":null,"abstract":"In this work, we consider the joint design of a surveillance radar and a multiple-input multiple-output communication system sharing the same bandwidth. In this framework, we maximize the energy efficiency at the communication system (i.e., the amount of information reliably delivered per unit of consumed energy) under a constraint on the minimum signal-to-disturbance ratio for each inspected range-azimuth resolution cell of the radar. The transmit powers of both systems, the space-time linear communication codebook, and the radar receive filters are the degrees of freedom for joint system optimization. The block coordinate ascent method is used to find an approximate solution to this optimization problem, and a numerical example is provided to show the merits of the proposed design strategy.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"6 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89202762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When the near-field underwater acoustic image (UAI) measurement is carried out by the line array laid on the sea floor, the resolution of the conventional beamforming (CBF) acoustic image measurement method is poor, the sidelobe level is high, while the deconvolution algorithm has the effect of high resolution and low sidelobe. However, the direct deconvolution algorithm of the point spread function (PSF) shift-variant model has a large computational burden. This paper presents a non-uniform spatial resampling Richardson-Lucy (RL) fast algorithm, which based on energy distribution of conventional acoustic image measurement result make the original uniform space scanning to non-uniform spatial resampling. It can reduce the number of scanning grid, so as to reduce the amount of computation. Simulation results show that the fast RL algorithm can achieve the performance close to the original RL algorithm by reducing the computational amount by nearly an order of magnitude.
{"title":"The Underwater Acoustic Image Measurement Based on Non-uniform Spatial Resampling RL Deconvolution","authors":"Jidan Mei, Yuqing Pei, Chao Ma, Yunfei Lv, Qiuying Peng","doi":"10.1109/SAM48682.2020.9104361","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104361","url":null,"abstract":"When the near-field underwater acoustic image (UAI) measurement is carried out by the line array laid on the sea floor, the resolution of the conventional beamforming (CBF) acoustic image measurement method is poor, the sidelobe level is high, while the deconvolution algorithm has the effect of high resolution and low sidelobe. However, the direct deconvolution algorithm of the point spread function (PSF) shift-variant model has a large computational burden. This paper presents a non-uniform spatial resampling Richardson-Lucy (RL) fast algorithm, which based on energy distribution of conventional acoustic image measurement result make the original uniform space scanning to non-uniform spatial resampling. It can reduce the number of scanning grid, so as to reduce the amount of computation. Simulation results show that the fast RL algorithm can achieve the performance close to the original RL algorithm by reducing the computational amount by nearly an order of magnitude.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"15 15","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91446646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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